In [1]:
import numpy as np
In [2]:
classnum = np.arange(1, 25)
classnum = np.delete(classnum, np.where(classnum == 20))
classnum
Out[2]:
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 21, 22, 23, 24])
In [3]:
np.random.choice(classnum, size = 3, replace = False)
Out[3]:
array([ 2,  6, 14])
In [4]:
np.random.choice(classnum, size = 1, replace = False)
Out[4]:
array([12])

image-2.png

In [5]:
# import plotly.io as pio
# pio.renderers.default='notebook'
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

Plotly¶

In [6]:
import pandas as pd
df = pd.DataFrame(data={'Time':[1100, 1110, 1120, 1130, 1140, 1150],
                       'Temperature':[30.0, 30.05, 30.06, 30.1, 30.14, 30.23]})
df
Out[6]:
Time Temperature
0 1100 30.00
1 1110 30.05
2 1120 30.06
3 1130 30.10
4 1140 30.14
5 1150 30.23
In [7]:
import plotly.express as px
fig = px.scatter(df, x="Time", y="Temperature", trendline="ols")
fig.show()

Calculate the value at 1200¶

image.png

In [8]:
0.00417143 * 1200 + 25.4038
Out[8]:
30.409516
In [ ]:

Seaborn¶

In [9]:
df
Out[9]:
Time Temperature
0 1100 30.00
1 1110 30.05
2 1120 30.06
3 1130 30.10
4 1140 30.14
5 1150 30.23
In [10]:
import seaborn as sns
sns.regplot(data = df, x="Time", y="Temperature" )
Out[10]:
<Axes: xlabel='Time', ylabel='Temperature'>
No description has been provided for this image
In [ ]:

Matplotlib¶

In [11]:
yearmonth = pd.Index(['2024-03','2024-04','2024-05','2024-06','2024-07','2024-08'])
actualorder = pd.Series([2305, 2108, 2375, 2422, 2493, 2532], index=yearmonth)
predictorder = pd.Series([2355, 2100, 2365, 2445, 2499, 2565], index=yearmonth)
In [12]:
df = pd.DataFrame(data={    
    'ActualOrder': actualorder,
    'PredictOrder': predictorder
})
df
Out[12]:
ActualOrder PredictOrder
2024-03 2305 2355
2024-04 2108 2100
2024-05 2375 2365
2024-06 2422 2445
2024-07 2493 2499
2024-08 2532 2565
In [13]:
import matplotlib.pyplot as plt
plt.plot(yearmonth, actualorder, label='Actual Order')
plt.plot(yearmonth, predictorder, '--', label='Predict Order')
plt.ylabel('Order in month')
plt.xlabel('2024 Order Forecast')
plt.grid(True)
plt.legend()
plt.title('Order Forecast vs Actual Order as of Aug 2024')
Out[13]:
Text(0.5, 1.0, 'Order Forecast vs Actual Order as of Aug 2024')
No description has been provided for this image

Alternative Answer¶

In [14]:
import matplotlib.pyplot as plt
plt.plot(df.ActualOrder, label='Actual Order')
plt.plot(df.PredictOrder, '--', label='Predict Order')
plt.ylabel('Order in month')
plt.xlabel('Fiscal Month')
plt.grid(True)
plt.legend()
plt.title('Order Forecast vs Actual Order as of Aug 2024')
Out[14]:
Text(0.5, 1.0, 'Order Forecast vs Actual Order as of Aug 2024')
No description has been provided for this image

Alternative in Seaborn¶

In [15]:
import warnings
warnings.filterwarnings("ignore")

sns.lineplot(df)
plt.grid(True)
plt.title('Order Forecast vs Actual Order as of Aug 2024')
Out[15]:
Text(0.5, 1.0, 'Order Forecast vs Actual Order as of Aug 2024')
No description has been provided for this image

Alternative in Plotly¶

In [16]:
px.line(df)
In [ ]:

Numpy¶

In [17]:
arr2D = np.array([[2, 3], 
                  [4, 5], 
                  [6, 7]
                 ], dtype=np.float64)
arr2D
Out[17]:
array([[2., 3.],
       [4., 5.],
       [6., 7.]])
In [18]:
arr2D * 2.1
Out[18]:
array([[ 4.2,  6.3],
       [ 8.4, 10.5],
       [12.6, 14.7]])
In [19]:
arr2D.shape
Out[19]:
(3, 2)
In [20]:
arr2D > 5
Out[20]:
array([[False, False],
       [False, False],
       [ True,  True]])
In [ ]:
In [ ]:

Pandas¶

In [21]:
import pandas as pd
yearmonth = pd.Index(['2024-03','2024-04','2024-05','2024-06','2024-07','2024-08'])
actualorder = pd.Series([2305, 2108, 2375, 2422, 2493, 2532], index=yearmonth)
predictorder = pd.Series([2355, 2100, 2365, 2445, 2499, 2565], index=yearmonth)
In [22]:
df = pd.DataFrame(data={
    'ActualOrder': actualorder,
    'PredictOrder': predictorder
    })
df
Out[22]:
ActualOrder PredictOrder
2024-03 2305 2355
2024-04 2108 2100
2024-05 2375 2365
2024-06 2422 2445
2024-07 2493 2499
2024-08 2532 2565
In [23]:
df.loc[(df.index>='2024-06') & (df.ActualOrder>2450)]
Out[23]:
ActualOrder PredictOrder
2024-07 2493 2499
2024-08 2532 2565
In [24]:
df.loc[df.index=='2024-04', 'ActualOrder'] = 2018
df
Out[24]:
ActualOrder PredictOrder
2024-03 2305 2355
2024-04 2018 2100
2024-05 2375 2365
2024-06 2422 2445
2024-07 2493 2499
2024-08 2532 2565
In [ ]:
In [ ]:

All¶

In [25]:
detailOrder = pd.DataFrame(data={
    'OrderDate' : ['2024-09-01','2024-09-01', '2024-09-03', '2024-09-01'],
    'OrderID' : [1002, 1002, 1005, 1002],
    'ProductID' : [111, 222, 555, 333],
    'Quantity' : [3, 2, 1, 4],
    'ClientID' : [888, 888, 999, 888],
})
detailOrder
Out[25]:
OrderDate OrderID ProductID Quantity ClientID
0 2024-09-01 1002 111 3 888
1 2024-09-01 1002 222 2 888
2 2024-09-03 1005 555 1 999
3 2024-09-01 1002 333 4 888
In [26]:
products = pd.DataFrame(data={
    'ProductID': [111, 222, 333, 444, 555],
    'ProductName': ['Python 101', 'Machine Learn ABC', 'Data Science Deep Dive',
                   'Neural Network', 'Happiness in Programming']
})
products
Out[26]:
ProductID ProductName
0 111 Python 101
1 222 Machine Learn ABC
2 333 Data Science Deep Dive
3 444 Neural Network
4 555 Happiness in Programming
In [27]:
clientInfo = pd.DataFrame(data={
    'ClientID' : [666, 777, 888, 999 ],
    'Name' : ['Alice', 'Bob', 'Charlie', 'Dave'],
    'Address' : ['23 Abs Street, Kowloon',
                 '37 Bon Road, NT',
                 '86 Caine Lane, Kowloon', 
                 '72 Doe Avenue, Hong Kong'],
    'Phone' : [234567, 456789, 567891, 789123],
})
clientInfo
Out[27]:
ClientID Name Address Phone
0 666 Alice 23 Abs Street, Kowloon 234567
1 777 Bob 37 Bon Road, NT 456789
2 888 Charlie 86 Caine Lane, Kowloon 567891
3 999 Dave 72 Doe Avenue, Hong Kong 789123
In [28]:
packageDF = detailOrder.merge(clientInfo, on='ClientID')
packageDF
Out[28]:
OrderDate OrderID ProductID Quantity ClientID Name Address Phone
0 2024-09-01 1002 111 3 888 Charlie 86 Caine Lane, Kowloon 567891
1 2024-09-01 1002 222 2 888 Charlie 86 Caine Lane, Kowloon 567891
2 2024-09-01 1002 333 4 888 Charlie 86 Caine Lane, Kowloon 567891
3 2024-09-03 1005 555 1 999 Dave 72 Doe Avenue, Hong Kong 789123
In [29]:
detailOrder.merge(clientInfo, how='left', on='ClientID')
Out[29]:
OrderDate OrderID ProductID Quantity ClientID Name Address Phone
0 2024-09-01 1002 111 3 888 Charlie 86 Caine Lane, Kowloon 567891
1 2024-09-01 1002 222 2 888 Charlie 86 Caine Lane, Kowloon 567891
2 2024-09-03 1005 555 1 999 Dave 72 Doe Avenue, Hong Kong 789123
3 2024-09-01 1002 333 4 888 Charlie 86 Caine Lane, Kowloon 567891
In [30]:
shipping = packageDF.merge(products, on='ProductID')[['OrderID', 'Name', 'Address', 'Phone', 'ProductName', 'Quantity']]
shipping
Out[30]:
OrderID Name Address Phone ProductName Quantity
0 1002 Charlie 86 Caine Lane, Kowloon 567891 Python 101 3
1 1002 Charlie 86 Caine Lane, Kowloon 567891 Machine Learn ABC 2
2 1002 Charlie 86 Caine Lane, Kowloon 567891 Data Science Deep Dive 4
3 1005 Dave 72 Doe Avenue, Hong Kong 789123 Happiness in Programming 1
In [31]:
packageDF.merge(products, on='ProductID').drop(['ProductID','ClientID','OrderDate'], axis=1)
Out[31]:
OrderID Quantity Name Address Phone ProductName
0 1002 3 Charlie 86 Caine Lane, Kowloon 567891 Python 101
1 1002 2 Charlie 86 Caine Lane, Kowloon 567891 Machine Learn ABC
2 1002 4 Charlie 86 Caine Lane, Kowloon 567891 Data Science Deep Dive
3 1005 1 Dave 72 Doe Avenue, Hong Kong 789123 Happiness in Programming
In [32]:
products[products['ProductName'].str.contains('Happiness')]
Out[32]:
ProductID ProductName
4 555 Happiness in Programming
In [33]:
studentIQ = np.array([94, 70, 72, 90, 95, 99, 103, 81, 80, 75, 102, 110, 130, 80, 83, 115, 86, 92, 99, 78, 108, ])
studentIQ
Out[33]:
array([ 94,  70,  72,  90,  95,  99, 103,  81,  80,  75, 102, 110, 130,
        80,  83, 115,  86,  92,  99,  78, 108])
In [34]:
studentIQ.mean()
Out[34]:
92.47619047619048
In [35]:
np.mean(studentIQ)
Out[35]:
92.47619047619048
In [36]:
studentIQ.std()
Out[36]:
15.085846560266656
In [37]:
np.std(studentIQ)
Out[37]:
15.085846560266656
In [38]:
studentIQ.min()
Out[38]:
70
In [39]:
np.min(studentIQ)
Out[39]:
70
In [42]:
np.count_nonzero(studentIQ > 90)
Out[42]:
11
In [54]:
np.where(studentIQ>90,1,0).sum()
Out[54]:
11
In [55]:
np.sum(studentIQ>90)
Out[55]:
11
In [56]:
# import warnings
# warnings.filterwarnings("ignore")

sns.histplot(studentIQ, kde=True)
Out[56]:
<Axes: ylabel='Count'>
No description has been provided for this image
In [ ]: